Recently, DETR [1]} provides a simple and clean pipeline for object detection. It formulates object detection as a set prediction task, and applies a transformer [2]} to translate sparse object candidates [3]} to the target objects. The success of DETR has sparked the recent surge of detection transformers [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]} and most of the following-up works focus on alleviating the slow convergence problem of DETR.
For example, DeformDETR [4]} propose the deformable attention mechanism for learnable sparse feature sampling and aggregates multi-scale features to accelerate model convergence and improve model performance.
CondDETR [7]} proposes to learn a conditional spatial query from the decoder embedding, which helps the model quickly learn to localize the four extremities for detection.
